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Filtering Spam Using Fuzzy Expert System

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TLDR
The fuzzy expert system performs to filter the spam and gives good results in terms of spam recall and precision.
Abstract
The rapid growth of users in the Internet and the abuse of e-mail by unsolicited users cause an exponential increase of spam in user mailboxes.. The techniques currently used by most anti-spam software are static; spammers simply examine the latest anti-spam techniques and find ways how to rip them off. This paper presents a fuzzy expert system to detect spam. Considering the pre-processing of the subject, content, the sender’s email address and attachments of the email to be ranked by using common spam words list. These ranked items represent the input variables for the proposed system, which classify the email as spam or not. The fuzzy expert system performs to filter the spam and gives good results in terms of spam recall and precision.

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References
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Proceedings Article

A Bayesian Approach to Filtering Junk E-Mail

TL;DR: This work examines methods for the automated construction of filters to eliminate such unwanted messages from a user’s mail stream, and shows the efficacy of such filters in a real world usage scenario, arguing that this technology is mature enough for deployment.
Proceedings Article

Web Spam Taxonomy

TL;DR: This paper presents a comprehensive taxonomy of current spamming techniques, which it is believed can help in developing appropriate countermeasures.
Journal ArticleDOI

Review: A review of machine learning approaches to Spam filtering

TL;DR: A comprehensive review of recent developments in the application of machine learning algorithms to Spam filtering, focusing on both textual- and image-based approaches concludes that while important advancements have been made in the last years, several aspects remain to be explored, especially under more realistic evaluation settings.
Posted Content

An Experimental Comparison of Naive Bayesian and Keyword-Based Anti-Spam Filtering with Personal E-mail Messages

TL;DR: In this article, a Naive Bayesian classifier is trained automatically to detect spam messages, and a large collection of personal e-mail messages are made publicly available in "encrypted" form contributing towards standard benchmarks.
Proceedings ArticleDOI

An experimental comparison of naive Bayesian and keyword-based anti-spam filtering with personal e-mail messages

TL;DR: This work introduces appropriate cost-sensitive measures, and investigates at the same time the effect of attribute-set size, training-corpus size, lemmatization, and stop lists, issues that have not been explored in previous experiments.
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